CN110175390A - A kind of RTDS artificial resource distribution method and its system based on the analysis of more knapsack problems - Google Patents
A kind of RTDS artificial resource distribution method and its system based on the analysis of more knapsack problems Download PDFInfo
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Abstract
The invention discloses a kind of RTDS artificial resource distribution methods and its system based on the analysis of more knapsack problems, and method is the following steps are included: be divided into a number of sub-network in the section part for meeting Decoupling Conditions for power grid；Emulation storage unit total resources needed for calculating power network modeling, each RACK can use storage resource calculated result；And calculate the RACK quantity of electric network model needs；The sub-network formed will be divided and be abstracted as object to be installed, each RACK is abstracted as knapsack, establishes more knapsack problem mathematical models of RTDS artificial resource distribution；More knapsack dynamic programming algorithms of RTDS artificial resource distribution are solved, and carry out RTDS artificial resource distribution.The present invention can effectively assist Modeling and Design personnel assignment RTDS artificial resource by quantitative analysis, promote the reasonability of RACK resource allocation and the stability of simulation run.
Description
Technical Field
The invention belongs to the technical field of real-time digital simulation of power systems, and particularly relates to a method and a system for distributing Real Time Digital System (RTDS) simulation resources based on multi-backpack problem analysis.
Background
With the rapid development of extra-high voltage direct current, the power grid in China already enters the stages of alternating-current and direct-current hybrid power transmission and regional power grid interconnection operation, which plays an important role in realizing the comprehensive optimization configuration of resources in a large range. But at the same time, in the construction transition period of the extra-high voltage power grid, the characteristic of strong direct current and weak alternating current of the power grid is obvious, and the safe operation of the system faces a larger risk. Under the new pattern of multiple direct current intensive feed-in, the degree of dependence on receiving external power is continuously improved, the external power enters a new normal state along with economic development, a large number of local units of an extra-high voltage direct current receiving end power grid are shut down under the background of slow load increase, once faults such as extra-high voltage direct current blocking occur, power shortage caused by the faults is very likely to cause over-stable limited operation of a local power grid, and cascading faults or large-range power failure may be induced under severe conditions.
In order to resist the impact of severe faults of an extra-high voltage direct current system on a regional power grid, national power grid companies are involved in developing system protection engineering construction, comprehensively utilizing full-grid control resources, coping with severe faults of the power grid, and improving the stability level of the power grid. The control layer architecture of the system protection engineering is complex, control information needs to pass through multiple levels and is long in transmission distance, a primary power grid model needs to be established based on RTDS (real-time digital simulation system, which is composed of multiple simulation units called rack), and the action characteristics and the effectiveness of a control strategy of the system in an actual large power grid are verified through real-time digital simulation.
However, a large amount of RTDS simulation resources are consumed for building a large power grid model, and especially under the condition that the number of RTDS is limited, how to plan and utilize the RTDS simulation resources to ensure the orderly development of various experiments is a problem that needs to be solved by modeling personnel.
Regarding the problem, a method for realizing effective planning of RTDS simulation resources through quantitative analysis is still lacking at present, and usually, a modeler can only allocate the use of the RTDS simulation resources by a primary power grid model according to past experience, so that the problems that the use of RACKs (RTDS simulators with certain resource storage and real-time operation capability) is unbalanced, and even the use of individual RACKs overflows exist.
Disclosure of Invention
The invention aims to: in order to solve the defect that RACK resources can only be qualitatively allocated by relying on traditional experience in the real-time digital simulation modeling process of a large power grid, the RTDS simulation resource allocation method and the RTDS simulation resource allocation system based on multi-backpack problem analysis are provided, and can effectively assist modeling designers to allocate RTDS simulation resources through quantitative analysis, so that the rationality of RACK resource allocation and the stability of simulation operation are improved.
Specifically, the invention is realized by adopting the following technical scheme, and the RTDS simulation resource allocation method based on multi-knapsack problem analysis comprises the following steps:
dividing the power grid into a plurality of sub-networks at the section meeting the decoupling condition; abstracting the sub-networks formed by the division into objects to be loaded, abstracting each RACK into a backpack, and establishing a multi-backpack problem mathematical model for RTDS simulation resource allocation;
and designing a bag dynamic planning algorithm for solving the multi-backpack problem mathematical model, and performing RTDS simulation resource allocation.
Dividing the power grid into a plurality of sub-networks at the section meeting the decoupling condition, and counting the number of various elements of the primary power grid and the number of RTDS simulation resources occupied by the corresponding models of the elements according to the scale of the experimental power grid before the step.
Dividing the power grid into a plurality of sub-networks at the section meeting the decoupling condition, and specifically comprising the following steps:
according to the topological composition of an experimental power grid, the power grid is divided into a plurality of sub-networks at a section meeting a decoupling condition, the scale of each sub-network is between [ C1, C2] simulation storage units, wherein C1 and C2 respectively represent the lower limit and the upper limit of the number of simulation storage units of the size of the sub-network scale.
The sections meeting the decoupling conditions are all formed by circuits, and the circuit reactance x and the ground susceptance C meet the following equation:
in the formula (f)bFor the reference frequency, τ is the simulation step.
After the step of dividing the power grid into a plurality of sub-networks at the section meeting the decoupling condition, the method also comprises the step of calculating the total resource amount R of the simulation storage unit required by the power grid modelingallCalculating the result r with the available storage resource of each RACKableAnd calculating the RACK quantity N required by the primary power grid modelumThe method comprises the following specific steps:
calculating the total resource amount R of the simulation storage unit required by power grid modeling according to the number of various elements contained in the power grid and the resource occupation condition of each element modelall；
According to the resource r consumed by the networklostRACK Reserve resource requirement rreserveDetermining the available resource r for each RACKable＝n*300-rlsot-rreserveAnd measuring the total quantity N of RACK resources required by power grid modelingum(ii) a Wherein n is the number of the single RTDS authorization simulation cores minus 1;
calculating RACK number required by power grid modelingIf the calculation result is thatThe remainder is required to be rounded and added with 1, and finally the RACK number N required by the primary power grid model is calculatedum。
Before the steps of abstracting sub-networks formed by division into objects to be loaded, abstracting each RACK into a backpack and establishing a multi-backpack problem mathematical model for RTDS simulation resource allocation, the method also comprises the following steps of determining a reward function of RTDS simulation resource allocation utility according to the principle that the connected sub-networks are allocated to the same RACK;
the method for determining the reward function of the RTDS simulation resource allocation utility comprises the following steps:
ith sub-network niWith the allocated set of sub-networks NdistriIn the absence of a connection, the penalty function will assign the sub-network n to the sub-network n in a reduced value manneriAnd a set of subnetworks NdistriPunishment is given to the same RACK, otherwise, reward is given; the penalty function takes the following values:
the penalty function of RTDS simulation resource allocation utility not only measures the value generated after the sub-network is added into the RACK, but also punishs the situation that the sub-network forms an isolated network after being added into the RACK.
The specific establishment method of the multi-knapsack problem mathematical model for RTDS simulation resource allocation comprises the following steps:
n th sub-networkiVolume attribute m of object after taking required RACK resource as abstractioniN is to beiThe relationship with other networks is denoted as set LiThe value of the reward penalty function is niLoading value v ofi；
Abstracting each RACK into a knapsack, wherein the available resource space of each knapsack is the actual size r of the knapsackableAnd according to the number N of the backpacks required by the power grid modelingum；
The multi-knapsack problem mathematical model for RTDS simulation resource allocation is expressed as follows:
max S[k][i],k＝1,2,L,Num
wherein r isable,kAvailable space for the kth backpack; s [ k ]][i]Value formed by putting the first i objects into a backpack k with the capacity of m; m [ k ]][i]The remaining capacity of backpack k after the first i objects are placed in backpack k.
The multi-backpack dynamic programming algorithm for solving the RTDS simulation resource allocation and the RTDS simulation resource allocation are carried out, and the specific method comprises the following steps:
during the solution process of loading objects in each iteration of a single backpack, the following principles are followed:
the object to be wrapped having a physical connection with the wrapped object, i.e. vi>0；
In the space m required by the ith objectiIf the bag is loaded under the condition of less than the residual space of the bag k, M k][i]If the maximum value is reached, the object i is put into the backpack; otherwise, the object i is not put into the backpack;
if the backpack k still has residual space, and the residual space is smaller than the required space of any object to be packaged, terminating the process of iteratively packaging the object by the kth backpack;
by planning RACK backpacks in sequence, solving the optimal space use scheme of objects loaded in each backpack, and acquiring RTDS simulation resource allocation of each subnet to a plurality of RACKs:
the invention relates to an RTDS simulation resource allocation system based on multi-knapsack problem analysis, which comprises a network interface, a memory and a processor; wherein,
the network interface is used for receiving and sending signals in the process of receiving and sending information with other external network elements;
the memory to store computer program instructions operable on the processor;
the processor is used for executing the steps of the RTDS simulation resource allocation method based on multi-knapsack problem analysis when the computer program instructions are run.
The invention has the following beneficial effects: according to the invention, RTDS simulation resource allocation is analyzed in a manner of combining qualitative and quantitative methods, on one hand, a power grid of resources to be allocated is decomposed into a plurality of subnets by an expert experience method, instead of taking a single element as an object, and factors such as power grid scale, network consumed resource solving and the like are comprehensively considered, so that the total RACK resource amount required by power grid modeling is determined, the iteration times of a planning algorithm can be greatly reduced, and the algorithm solving efficiency is improved; on the other hand, a multi-knapsack problem mathematical model of RTDS simulation resource allocation is established by utilizing a knapsack problem principle, a knapsack dynamic planning algorithm is designed to solve the multi-knapsack problem of RTDS simulation resource allocation, modeling designers can be effectively assisted to allocate RTDS simulation resources through quantitative analysis, and the reasonability of RACK resource allocation and the stability of simulation operation are improved. The method realizes quantitative analysis and calculation of RTDS simulation resources, can effectively assist electric power system modeling simulation personnel to effectively distribute the RTDS simulation resources at the initial modeling stage, overcomes the defect that large-scale power grid RTDS modeling at the present stage excessively depends on expert experience, and improves modeling efficiency and resource utilization refinement level.
Drawings
FIG. 1 is a flowchart illustrating a method for allocating RTDS simulation resources in this embodiment;
FIG. 2 is a flow chart of a multi-backpack dynamic programming algorithm for solving RTDS simulation resource allocation in accordance with the present invention;
FIG. 3 is a schematic diagram of a topology of a regional grid sub-network;
FIG. 4 is a diagram of a power grid model RTDS simulation resource allocation use case.
Detailed Description
The present invention will be described in further detail with reference to the following examples with reference to the accompanying drawings.
Taking a regional power grid (as shown in fig. 3) as an example, the resources occupied by each element are counted according to step 1 of the method, the regional power grid is divided into 20 sub-networks of a, B, C, … and Q by using expert experience, and the topological connection relationship among the 20 sub-networks is given in fig. 3.
Referring to fig. 1, the RTDS simulation resource allocation method of the present invention specifically includes the following steps:
1) and counting available simulation resources of the RTDS, and setting the authorized simulation core number n of each RACK configuration to be 3. Except that the core 1 is used for solving the network, 300 simulation memory units are left for each authorized simulation core, and each RACK has 600 simulation memory units. According to the resource conditions (see table 1) occupied by different elements in the RTDS simulation environment, the RTDS simulation resources occupied by various element models of the primary power grid are counted.
TABLE 1 situation of RTDS simulation resource occupation by various component models
| Serial number | Component type | Number of occupied memory cells |
| 1 | Load of motor | 20 |
| 2 | Static load | 10 |
| 3 | Transformer device | 10 |
| 4 | Line | 10 |
| 5 | Synchronous generator | 20 |
2) Firstly, adopting expert experience method to decompose the power network into several sub-networks, and setting the scale of each sub-network between [ C1, C2]]Among the simulated memory cells, here C1 is calculated as 300 and C2 is calculated as 500. Then calculating RACK total resource R required by modeling according to the quantity of various elements contained in the power grid and the resource occupation condition of each element modelallWhile analyzing the resources r consumed by the solution networklostRACK Reserve resource requirement rreserveDefining the available resource r for each RACKable＝n*300-rlsot-rreserveAnd then measuring the total amount of RACK resources required by power grid modeling
Occupation resources of each sub-network (i.e. abstract as volume attribute m of object)i) And the sub-network is connected to other networks (i.e., set L)i) The information of the same is shown in table 2. For example, m of the 1 st sub-network A1＝80、Li＝{C,D}。
TABLE 2 RTDS simulation resources and topology information required for each subnetwork
| Serial number | Sub-network name | Sub-network occupied resources | Connected sub-network names |
| 1 | A | 80 | C、D |
| 2 | B | 90 | D、E |
| 3 | C | 90 | A、G |
| 4 | D | 80 | A、B、E、F |
| 5 | E | 100 | B、D、H |
| 6 | F | 80 | D、G、H |
| 7 | G | 100 | C、F、H、J、M |
| 8 | H | 100 | E、F、G、I、J、K |
| 9 | I | 90 | H |
| 10 | J | 90 | G、H、K、L |
| 11 | K | 80 | H、J、N |
| 12 | L | 100 | J、O |
| 13 | M | 100 | G、O |
| 14 | N | 80 | K、O |
| 15 | O | 90 | M、L、N |
From Table 2, the total amount of resources of the emulated storage unit required for each sub-network of the implementation case can be calculated
Resource r for setting each RACK for solving networklost120, RACK reserved resource requirement rreserveThen the available memory resource per RACK is calculated as r 50able＝600-rlsot-rreserve430 memory cells. In defining RACK Total storage resource requirement RallAnd each RACK available storage resource rableOn the basis, the RACK number required by power grid modeling is calculatedThe calculation result has remainder, the integer is required to be taken and added with 1, and finally N is required to be distributed for calculating and establishing the primary power grid model of the embodimentum4 RACK.
3) Establishing RTDS simulation resource allocation utility reward function f according to the principle that connected sub-networks should be allocated to the same RACK as much as possiblepun(ni,Ndistri) When the ith sub-network niWith the allocated set of sub-networks NdistriWhen there is a connection relation, the distribution utility is increased to the sub-network niAnd a set of subnetworks NdistriDistributing the RACK to the same RACK for reward; when the ith sub-network niWith the allocated set of sub-networks NdistriIn the absence of a connection, the sub-network n is paired by reducing the assigned utilityiAnd a set of subnetworks NdistriAllocating the result to the same RACK to make punishment; the purpose of punishment is to avoid forming an isolated network after a sub-network is added into RACK. The penalty function takes the following values:
4) abstracting each sub-network formed by division into objects to be loaded by utilizing the knapsack problem idea, and enabling the ith sub-network niVolume attribute m of object after taking required RACK resource as abstractioniN is to beiThe relationship with other networks is denoted as set LiThe value of the reward penalty function is niLoading value v ofi(ii) a Setting S [ k ] simultaneously][i]Value is built into the backpack k with a capacity of m for the first i objects.
Abstracting each RACK into a knapsack, wherein the available resource space of the knapsack is the actual size r of the knapsackableAnd according to RACK resource total quantity N required by power grid modelingumI.e. number of backpacks; setting M [ k ] at the same time][i]The residual capacity of the backpack k after the first i objects are placed into the backpack k; then, a multi-knapsack problem mathematical model for RTDS simulation resource allocation is established, and on the basis, the multi-knapsack problem mathematical model for RTDS simulation resource allocation of the regional power grid in the case can be expressed as follows:
max S[k][i],k＝1,2,3,4
wherein r isable,kIs the available space of the kth backpack, i.e. the available resources of the kth RACK.
5) And designing a multi-knapsack dynamic planning algorithm for solving RTDS simulation resource allocation. The reasonable arrangement of the space of the plurality of backpacks is realized by loading objects into the backpacks one by one. The dynamic programming algorithm solving steps are as follows:
step 1: initializing 1 for both an object i to be loaded and a backpack k;
step 2: value S [ k ] formed by setting the first i objects to be put into backpack k with capacity m][i]0; after the first i objects are put into the backpack k, the remaining capacity M [ k ] of the backpack k][i]＝rable；
And step 3: in the space m required by the ith objectiIf the packing scheme is loaded in the condition of less than the residual space of the knapsack k, the value is the maximum, namely M [ k ]][i]If the maximum value is reached, the object i is put into the backpack; otherwise, the object i is not put into the backpack;
and 4, step 4: the object to be packed has as physical a relationship as possible with the packed object, i.e. vi>0；
And 5: if the backpack k still has residual space, and the residual space is smaller than the required space of any one object to be packaged, terminating the process of iteratively packaging the object by the kth backpack, otherwise, executing the step 3.
Step 6: and (4) judging whether all the backpacks are distributed completely, if so, finishing the algorithm, and otherwise, executing the step (2).
By pairing NumAnd (4) sequentially planning and solving the optimal space use scheme of each backpack, namely the resource allocation scheme that each subnet is deployed to a plurality of RACKs. The algorithm flow proposed by the design of the invention is shown in fig. 2.
The algorithm is mainly calculated as shown in formulas (3) and (4).
The subnet modeling distribution result for solving 4 RACKs by using the multi-backpack dynamic programming algorithm designed by the invention is shown in Table 3. Resource usage of 4 RACKs as shown in FIG. 4, the resource usage of each RACK does not exceed its available storage resource threshold rableAnd the allocation and use of 4 RACK resources are more balanced.
TABLE 3 Allocation and usage of RACK resources
| Serial number | RACK name | Sub-network names assigned to respective RACKs |
| 1 | RACK1 | A、C、G、J |
| 2 | RACK2 | B、D、E、F |
| 3 | RACK3 | I、H、K、N |
| 4 | RACK4 | M、O、L |
The invention relates to an RTDS simulation resource allocation system based on multi-knapsack problem analysis, which comprises a network interface, a memory and a processor; wherein,
the network interface is used for receiving and sending signals in the process of receiving and sending information with other external network elements;
the memory to store computer program instructions operable on the processor;
the processor is used for executing the steps of the RTDS simulation resource allocation method based on multi-knapsack problem analysis when the computer program instructions are run.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present invention has been described in connection with the preferred embodiments, it is not intended to be limited thereto. Any equivalent changes or modifications made without departing from the spirit and scope of the present invention also belong to the protection scope of the present invention. The scope of the invention should therefore be determined with reference to the appended claims.
Claims (10)
1. An RTDS simulation resource allocation method based on multi-knapsack problem analysis is characterized by comprising the following steps:
dividing the power grid into a plurality of sub-networks at the section meeting the decoupling condition; abstracting the sub-networks formed by the division into objects to be loaded, abstracting each RACK into a backpack, and establishing a multi-backpack problem mathematical model for RTDS simulation resource allocation;
and solving a dynamic programming algorithm of the multi-knapsack problem mathematical model to distribute RTDS simulation resources.
2. The RTDS simulation resource allocation method based on multi-knapsack problem analysis according to claim 1, wherein before the step of dividing the grid into a number of sub-networks at the section satisfying the decoupling condition, further comprising: and counting the number of various elements of the primary power grid and the number of RTDS simulation resources occupied by the corresponding models of the elements according to the scale of the experimental power grid.
3. The RTDS simulation resource allocation method based on multi-knapsack problem analysis according to claim 1, characterized by dividing the power grid into a number of sub-networks at the section satisfying the decoupling condition, the specific steps are as follows:
according to the topological composition of the experimental power grid, the power grid is divided into a plurality of sub-networks at the section meeting the decoupling condition, the scale of each sub-network is between [ C1, C2] simulation storage units, wherein C1 and C2 respectively represent the lower limit and the upper limit of the number of simulation storage units of the size of the sub-network scale.
4. The RTDS simulation resource allocation method based on multi-knapsack problem analysis according to claim 3, characterized in that the sections satisfying the decoupling condition are all composed of lines, and the line reactance x and ground susceptance C satisfy the following equation:
in the formula (f)bFor the reference frequency, τ is the simulation step.
5. The RTDS simulation resource allocation method based on multi-knapsack problem analysis according to claim 3, characterized in that after the step of dividing the grid into a number of sub-networks at the section satisfying the decoupling condition, the method further comprises calculating the total amount R of simulation storage unit resources required by grid modelingallCalculating the result r with the available storage resource of each RACKableAnd calculate out oneRACK quantity N required by sub-grid modelumThe method comprises the following specific steps:
calculating the total resource amount R of the simulation storage unit required by power grid modeling according to the number of various elements contained in the power grid and the resource occupation condition of each element modelall；
According to the resource r consumed by the networklostRACK Reserve resource requirement rreserveDetermining the available resource r for each RACKable＝n*300-rlsot-rreserveAnd measuring the total quantity N of RACK resources required by power grid modelingum(ii) a Wherein n is the number of the single RTDS authorization simulation cores minus 1;
calculating RACK number required by power grid modelingIf the calculation result has a remainder, the integer is required to be increased by 1.
6. The RTDS simulation resource allocation method based on multi-knapsack problem analysis according to claim 1,
before the steps of abstracting sub-networks formed by division into objects to be loaded, abstracting each RACK into a backpack and establishing a multi-backpack problem mathematical model for RTDS simulation resource allocation, the method also comprises the following steps of determining a reward function of RTDS simulation resource allocation utility according to the principle that the connected sub-networks are allocated to the same RACK;
the method for determining the reward function of RTDS simulation resource allocation utility comprises the following steps:
ith sub-network niWith the allocated set of sub-networks NdistriIn the absence of a connection, the penalty function will assign the sub-network n to the sub-network n in a reduced value manneriAnd a set of subnetworks NdistriPunishment is given to the same RACK, otherwise, reward is given; the penalty function takes the following values:
7. the RTDS simulation resource allocation method based on multi-knapsack problem analysis according to claim 6, wherein the reward function of RTDS simulation resource allocation utility is used to measure the value generated after the sub-network is added to the RACK, and the reward function of RTDS simulation resource allocation utility is also used to punish the situation that the sub-network forms the isolated net after being added to the RACK.
8. The RTDS simulation resource allocation method based on multi-knapsack problem analysis according to claim 1, characterized in that the multi-knapsack problem mathematical model for RTDS simulation resource allocation is specifically established as follows:
n th sub-networkiVolume attribute m of object after taking required RACK resource as abstractioniN is to beiThe relationship with other networks is denoted as set LiThe value of the reward penalty function is niLoading value v ofi；
Abstracting each RACK into a knapsack, wherein the available resource space of each knapsack is the actual size r of the knapsackableAnd according to the number N of the backpacks required by the power grid modelingum；
The multi-knapsack problem mathematical model for RTDS simulation resource allocation is expressed as follows:
wherein r isable,kAvailable space for the kth backpack; s [ k ]][i]Value formed by putting the first i objects into a backpack k with the capacity of m; m [ k ]][i]The remaining capacity of backpack k after the first i objects are placed in backpack k.
9. The RTDS simulation resource allocation method based on multi-knapsack problem analysis according to claim 8, wherein the dynamic programming algorithm for solving the multi-knapsack problem mathematical model and performing RTDS simulation resource allocation specifically comprises the following steps:
during the solution process of loading objects in each iteration of a single backpack, the following principles are followed:
the object to be wrapped having a physical connection with the wrapped object, i.e. vi>0；
In the space m required by the ith objectiIf the bag is loaded under the condition of less than the residual space of the bag k, M k][i]If the maximum value is reached, the object i is put into the backpack; otherwise, the object i is not put into the backpack;
if the backpack k still has residual space, and the residual space is smaller than the required space of any object to be packaged, terminating the process of iteratively packaging the object by the kth backpack;
by pairing NumSequentially planning the backpacks, solving the optimal space use scheme of objects loaded in each backpack, and acquiring RTDS simulation resource allocation of each subnet to a plurality of RACKs:
10. an RTDS simulation resource allocation system based on multi-knapsack problem analysis, characterized in that the system comprises a network interface, a memory and a processor; wherein,
the network interface is used for receiving and sending signals in the process of receiving and sending information with other external network elements;
the memory to store computer program instructions operable on the processor;
the processor, when executing the computer program instructions, is configured to perform the steps of the multi-knapsack problem analysis based RTDS simulation resource allocation method according to any of claims 1 to 9.
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